{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'accuracy rate of predict age')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UAvL/JP0bqCjpaqLngF/Mp4xzzjn3s+xs4/Uvv+eRj+YAcF+XI7n4+HoU24cXg9jTUpnU9bik04D1RPeR/5p4MYZzzjmXnwUZG+k9cBrp36/h5CbV+EfXFtSutO8vBrGnpbS4RAjAHoSdc86lbEdWNv3GL+Sp0d9ycMniPH7B0XRrXWu/ev/0npRrQJa0gdxf5IGZVSiQFjnnnNvnzVi6jt6DpjFz2XrOPKom93Y5kurlSxd2s4q0XAOymZUHCI8p/Uj0BigBvYDye6V1zjnn9ilbd2Txr9Hf8u/xC6lcthQvXNSazi323/dP70mpDFn/3syOi20/L+kr4NHcCjjnnDvwfLNoNb0HTWNhxiYuaFObu89qziFlShZ2s/YZqQTkLEm9iFZrMqIXUyS/ytE559wBauO2TB79aA6vTfie2pUO5vUr29K+sb/7flelEpD/D3gq/BjR+ro7vQLTOefcgWfcvAz+Mng6y9Zt4bIT6nP775tS9qCU5gu7JKk89rSIvNcxds45d4BZu3k7978/i8GTlnJ4tbIMvLYdbeodWItB7GmpvMu6GnA1UD+e38yuKLhmOeecK6qGT1/OX9+bwdrNO7jxlEbceGqjA3IxiD0tlXGF94jW5R2F3zt2zrkD1or1W/nrezP5aOaPtKhVgf5XtOXIww7cxSD2tFQCchkz613gLXHOOVckmRnvTFzCg+/PYltmNn3OaMZVJzWgxH66GERhSeVqvi/pzN2pXFJnSXMlzZfUJ4f9fSVNCT/zJK1N2l9B0lJJz8TSxoY6E+Wqh/TLJGXE0q+KlcmKpQ+NpZ8qaZKkGZL6SyoR0itJGiJpmqSvJbUI6U1j9UyRtF7Sn2P1/Sm0baakR0NaFUmfSNoYP4+k8xwqaUZs+2hJEyRNlzRMUoWQ3jZ27KmSusbKLAr5p0hKj6VXlvSxpG/D70pJxz42XJ/uuf8lnXMHqsWrN3PJK19zx8BpNKtZgQ9vbs+1HQ73YFwAUukh3wz8RdJ2YDvRy0Esvzd1SSoOPAucBiwBvpE01MxmJfKY2S2x/H8iWuYx7gFgXA7V9zKznNYBfNvMbswhfYuZtUpqXzGgP9DJzOaFF6BcCrwM/AWYYmZdJTUL59HJzOYCrWLntxQYErZPIZr81tLMtiW+KABbgXuAFuHnVySdD2xMSn4JuM3Mxkm6Arg91DEDSAtrTR8KTJU0zMwyQ7lTclhruQ8w2sweDl+K+hCtKZ04h0eI1qx2zrmfZWUbr01YxGMj5iLggfNa0KttXV8MogDl+xXHzMqbWTEzK21mFcJ2Kq/NbAvMN7OFZrad6DnmvGZr9wTeSmxIagPUAEamcKzdUQXYZmbzwvbHQLfwuTkwGsDM5gD1JdVIKt8JWGBm34ft64CHzWxbKLci/N5kZp8RBeZfkVQOuBV4MGlXU2B8crvMbHMs+JYmj1ebxpxL9MWD8Pu82L4/AYOAFSnU45w7QMxfsYELXviC+4bNom2Dyoy8tYOvzLQX5BuQFblI0j1hu46ktinUXQtYHNteEtJyOkY9oAEwJmwXA54g6hnm5NUwNHuPfv2W8m5hmHmgpDqx9NKS0iV9KSkRkFYCJSUlFo3uDiTKTAXOD21pC9QDaie1oQexLxBAE6C9pK8kjZN0bC5tj3sgnOfmpPQZQJfw+YJYu5B0nKSZwHTg2liANmCkpImSronVVcPMlgOE34kh/lpAV+CFvBoo6Zpw7dIzMjJSOCXn3L5qR1Y2z4z5ljOf+oyFKzfR98KjefWyY6lV8eDCbtoBIZWbAM8B7fjlZSAbiYZw85PTV6ncenQ9gIFmlpjFfT0w3MwW55C3l5kdBbQPPxeH9GFAfTNrSTQjvH+sTF0zSwvn8KSkw83MwnH7Svoa2AAkgtvDQCVJU4h6kZNj+5BUiihgvhM7RgmgEnA80ReJ/yV9WfgVSa2ARmY2JIfdVwA3SJpI9N7w7YkdZvaVmR0JHAvcKSnxtvYTzaw1cEYoe3Juxw6eBHrHrnmOzKyfmaWZWVq1av7mHef2V9OXrOOcpz/j8ZHzOO3IGoy6tQNdj6ntKzPtRancQz7OzFpLmgxgZmtCQMrPEmI9O6Ie5rJc8vYAbohttyPqbV4PlANKSdpoZn3MbGloxwZJ/yUaGn/NzFbFyr9IdG+UkHdZ+L1Q0liie9ULzGwCUVBH0ulEvVzMbD1weUgX8F34STgDmGRmPyWd7+AQ6L+WlA1UBXLrVrYD2khaRPR3qC5prJl1DMPkp4fjNwHOSi5sZrMlbSK6L50eO8cVkoaE6zIe+EnSoWa2PNx3TgxPpwEDwv9sVYEzJWWa2bu5tNc5tx/auiOLJ0d9y4ufLqRK2VL8++I2/P7ImoXdrANSKj3kHWHyj8HPLwrJTqHcN0BjSQ1CAO8BDE3OJKkpUc9yQiLNzHqZWV0zqw/cRhRw+0gqIalqKFcSOJtoeJcQbBK6ALNDeiVJB4XPVYETgVlhOzF8exDRRKcXwnbF2JeOq4DxIUgn/Op+d/AucGoo3wQoRTQsniMze97MDgvneBIwz8w6JrWrGHB3rF0N9MtM8HpE95oXSSorKbE6V1miYJ6YtT2UaLIa4fd74fgNzKx+OP5A4HoPxs4dWL5auIoznvqUF8Yt4II2tfn41g4ejAtRKj3kfxHNJK4u6e9E91rvzq9QmAl8I9EM3uLAK2Y2M8xmTjezRHDuCQwIPcv8HASMCMG4ONHQ9Ith302SuhANLa8GLgvpRwD/Dj3WYkQTrxIzvW+XdHZIf97MxsTKvCYpiyh4X5logKQyRDPH/5jUtleAVxQ9vrQduDRxTqEXXIGop38ecHp8tnkOekpKjBgMBl4Nn08C+kjaQfSl6HozWympITAk9HZLAP81s49CmYeJhs+vBH4guiftnDuAbdi6g0c+msMbX/5AncoH8+ZVx3Fio6qF3awDnlKJg+HRn05E94VHm9nsgm6YK3rS0tIsPT2np82cc/uKT+as4K4h01m+fitXnNiA/3d6E8qU8sUgCpKkiWEeU55SeZd1ZaL7jvFHkkqa2Y7f1kTnnHN7y+pN23ng/VkMmbyUxtXLMei6E2hdt1L+Bd1ek8rXoklEk7PWEPWQKwLLJa0ArjaziQXYPuecc7+BmfH+tOXcO3Qm67bs4OZOjbn+lMM5qIQvBlHUpBKQPwKGmNkI+Hk2cmfgf0SPRB1XcM1zzjm3u35av5W7hsxg1OyfaFn7EN68+jia1UzlvU6uMKQSkNPM7NrEhpmNlPQPM7s1MXvZOedc0WFmvP3NYv4+fDbbM7O568wjuPzE+v7+6SIulYC8WlJvoldfAlwIrAmPQqXy+JNzzrm95IdVm+kzeBpfLFjF8Q0r8/D5LalftWxhN8ulIJWA/H/A34ieswX4LKQVB/5QQO1yzjm3C7KyjVc//47HR86lZLFi/KPrUfQ4to6/f3ofkm9ADqsH/SmX3fP3bHOcc87tqrk/buCOQdOYungtnZpV58GuLTj0EH//9L7GHz5zzrl91PbMbJ4bO59nP5lP+dIleapHK7ocfZi/f3of5QHZOef2QVMXr+WOgdOY+9MGzm11GH89uzlVyvk8231ZKi8GOdHMPs8vzTnnXMHbsj2Lf348l5c/+47q5Uvz8qVpdDoiebl2ty9KpYf8NNA6hTTnnHMF6IsFK+kzaDo/rN7M/x1Xlz5nNKNC6ZKF3Sy3h+QakCW1A04Aqkm6NbarAtEMa+ecc3vB+q07eGj4HN76+gfqVSnDW1cfT7vDqxR2s9weltdT4qWI1iIuAZSP/awnWvEpX5I6S5orab6kPjns7ytpSviZJ2lt0v4KkpZKeiaWVkpSv5B/jqRuIb2epNGSpkkaK6l2rExdSSMlzZY0S1L9kH6qpEmSZkjqH1vasJKkIaGuryW1COlNY+2dImm9pD+HffeGtib2nRnST5M0UdL08PvUHK7D0LBKVGL7aEkTQplhkirE9rUM+2aG/aXzqeuCkDdbUlosvb6kLbH2vpDf39M5t/eNmvUTp/1zHG9/8wPXnNyQj24+2YPxfirXHrKZjQPGSfqPmX2/qxWHF4c8S7RU4RLgG0lD48sOmtktsfx/Ao5JquYBYFxS2l3ACjNrEtYLrhzSHydaN7l/CHoPAReHfa8BfzezjyWVA7JD2f5AJzObF5aFvBR4GfgLMMXMuoaVrp4N+eYCrWLnt5RoacqEvmb2eFJ7VwLnmNmyENhHALVi530+sDGpzEvAbWY2TtIVwO3APeELwxvAxWY2VVIVYEc+dc0Azgf+zc4WmFmrHNKdc4Vs1cZt3DdsFkOnLqNZzfL0uziNo+tULOxmuQKUynvUXpL0838Fofc4IoVybYH5ZrbQzLYTvenr3Dzy9+TXK0q1AWoAI5PyXUEUbDGz7PCcNEBzYHT4/EniWJKaAyXM7ONQZqOZbQaqANvMbF4o8zHQLbkuM5sD1JeUPGuiE1FAy/PLiplNNrNlYXMmUDrxytHw5eBW4MGkYk2B8Tm063RgmplNDXWvMrOsvOoys9nhi4Rzbh9gZrw3ZSm/++c4PpyxnFtPa8LQG0/yYHwASCUgVzWzn4eSzWwNUD2FcrWAxbHtJcR6hnGS6gENgDFhuxjwBFHPMJ4v8V/kA2Go+Z1YoJzKL4GrK1A+9CCbAGslDZY0WdJjoXe7EigZG8btTrSqVaKu88Mx2wL1gJ+HwIMexL5ABDeGYe5XJOW0rlk3YLKZbUucRzjPzUn5ZgBdwucLYu1qApikEeH874iVya2uvDQI12ScpPa7UM45VwCWr9vClf3TuXnAFOpVKcsHN7Xnpk6NKVXC30F9IEjlr5wtqW5iIwRPS6FcTk+m51auBzAw0dsDrgeGm9nipHwliALj52bWGphANFQNcBvQQdJkoAPRcHJmKNM+7D8WaAhcZmYWjttX0tfAhpAf4GGgkqQpRG8pmxzbh6RSRAHznVjbngcOJxrSXk4UHImVORJ4BPhj2G4FNDKz+JB3whXADZImEt233x47/5OAXuF3V0md8qkrN8uBumZ2DFHP+r/xe9Wxdl8jKV1SekZGxi5U75xLVXa28eZX33PaP8czYcEq7jm7OYOuO4EmNcoXdtPcXpTKY093AZ9JStzLPRm4JoVyS/ilZwdRIF2WS94ewA2x7XZAe0nXE00sKyVpI3AnUQ8wEXjeAa4ECMPCiV5tOaCbma2TtISoV7ow7HsXOB542cwmEAXrxLKSTUJd64HLQ7qA78JPwhnAJDP7KZEQ/yzpReD92Hbt0OZLzGxB7BzbSFpE9HeoLmmsmXUMw+Snh7JNgLNi13RcYphe0nCix8825lZXLteb0EvfFj5PlLQgnH96Ur5+QD+AtLS0VL6IOed2wXcrN9Fn0DS++m41JzaqwkNdW1K3SpnCbpYrBPn2kM3sI6J/9N8mWgO5TWJt5Hx8AzSW1CD0KHsAQ5MzSWoKVCLq7SaO2cvM6ppZfaKe7Wtm1if0aocBHUPWTsCsUE/VMNQNUeB+JdaOSpKqhe1TY2Wqh98HAb2BF8J2xdBmgKuA8SFIJ/zqfncoc2hssyvRsHNimP0D4M74y1TM7HkzOyyc40nAvEQAjbWrGHB3ol1EE8JaSioTJnh1AGblVVduJFULQ/dIagg0BhbmVcY5t+dkZmXTb/wCOj85nlnL1/Not5a8ceVxHowPYHk9h9zMzOZISrwAJNG7rSuprplNyqtiM8uUdCNRECkOvGJmM8Ns5nQzSwTnnsCAEGxT0Rt4XdKTQAahJ0sUpB+SZEQTom4I7ciSdBswOvR2JwIvhjK3Szqb6IvJ82Y2JqQfAbwmKYsoeF8Zuy5liGaO/zGpXY+GoWMDFsX23wg0IpolfU9IO93MVuRxjj0lJUYMBgOvhnNZI+mfRF8yjGhY/4M86kFSV6IXuVQDPpA0xcx+TzTScb+kTCALuNbMVudVl3Nuz5i9fD29B01j2pJ1nNa8Bg+e14IaFUrnX9Dt15RbHJT0opldLemTHHabme30PK3bv6WlpVl6enr+GZ1zOdqWmcWzY+bz3NgFVCxTkvu6tODMo2r6YhD7OUkTzSwtv3x5PYd8dfh9yp5smHPOHYgm/bCG3gOn8e2KjZx/TC3uObs5lcqWyr+gO2DkNWSch2kHAAAgAElEQVR9fl4FzWzwnm+Oc87tXzZvz+TxEfN49YvvOLRCaV69/FhOaZrKk6PuQJPXLOtzwu/qRO+0TtxfPQUYS3Rv0znnXC4+n7+SPoOnsXj1Fi4+vh53dG5KeV8MwuUiryHrxGM/7wPNzWx52D6U6FWSzjnncrBuyw7+8cFs3k5fTIOqZXn7muM5rqG/f9rlLZXnkOsngnHwE+F5Xeecc782cuaP3P3uDFZt2s51HQ/n5k6NKV3SF8hz+UslII8N765+i+hRmx5E74p2zjkXZGzYxr3DZvLBtOUccWgFXr70WI6qfUhhN8vtQ/INyGZ2Y3iW9eSQ1G8XX9HonHP7LTNjyOSl3P/+LDZvy+L23zflmpMbUrK4v3/a7ZpUesgAk4ANZjYqvCWqvJltKMiGOedcUbd07RbuGjKdsXMzaF23Io92b0mj6v7+abd78g3Ikq4mend1ZaLFE2oRvcqxU8E2zTnniqbEYhAPfzgHA+49pzkXt6tP8WL+gg+3+1LpId9AtLbxVwBm9m3iXcvOOXegWZixkT6DpvP1otW0b1yVf3Q9ijqV/f3T7rdLJSBvM7PtiVe7hUUNfNUf59wBJTMrmxc//Y6+o+ZRukQxHuveku5tavtrL90ek0pAHifpL8DBkk4jWqt4WME2yznnio5Zy9Zzx6CpzFi6ns5H1uT+c4+kui8G4fawVAJyH6LVjqYTrWA0HHipIBvlnHNFwdYdWTwzZj4vjFtAxTKleL5Xa8446tD8Czq3G/Kclx/Wy33NzF40swvMrHv4nNKQtaTOkuZKmi+pTw77+0qaEn7mSVqbtL+CpKWSngnb5WP5p0haGZZhjJfpLskkpYXt0yRNlDQ9/D41lvdCSdMkzZT0aCy9nqTRYd9YSbVj+7Jixx8aS/+PpO9i+1qF9I6S1sXS/5p8jSVNDm9ES6SdKmmSpBmS+ofbBEiqJGlIaNfXklqE9NJhe2o4l/tidTWQ9JWkbyW9nVjnWdJlkjJi7boqlb+pcweKid+v5qx/fcozn8zn3Fa1GHXryR6MXYHKs4cc1hKuJqmUmW3flYpDMH+WaO3gJcA3koaa2axY/bfE8v8JOCapmgeAcbH8G4BWsTITib1TW1J54CbCBLRgJXCOmS0LAWwEUEtSFeAxoI2ZZYTA18nMRgOPE30R6R8C+EPAxaG+LWbWipzdbmYDc0j/1MzOzqXMzcBsoEI4h2JAf6CTmc1TtH70pcDLwF+AKWbWVVIzouvbCdgGnGpmGyWVBD6T9KGZfQk8AvQ1swGSXiAa7Xg+HPttM7sxl3Y5d0DatC2Tx0bMpf+ERRx2yMH0v6ItHZpUK+xmuQNAKk+uLwI+l3SPpFsTPymUawvMN7OFIZgPAM7NI39PoreBASCpDVADGJlTZkmNiRa++DSW/ADwKLA1kWBmk81sWdicCZSWdBDQEJhnZhlh3yigW/jcHBgdPn+ST7t3W+h5n8WvbwFUIZpINy9sf5xTu8xsDlBfUg2LbAx5SoYfUzTb5FQg8SWhP3BeQZyLc/uD8fMyOL3vePpPWMQlx9djxC0nezB2e00qAXkZ8H7IWz72k59awOLY9pKQthNJ9YAGhBWlQi/xCeD2POrvSdTDs1DmGKCOmb2fR5luwGQz2wbMB5pJqh+GhM8D6oR8U/klCHYFyoceNUQBPV3Sl5KSg9vfw3By3xD0E9qF4eQPJR0ZS38SuAPIjqWtBEomhtyB7kntOj+cb1ugHlA7bBeXNAVYAXxsZl8RBfe1ZpYZyif/DbqF9g6UVAfnDlDrNu/gtnemcskrX3NQyWL874/tuO/cFpQ7KNV3Jzn326Xy6sz7ILqfG22m/IaunJ4FyO3ecw9goJllhe3rgeFmtli5P1LQgzCMHAJ4X+CyXBsTBcJHgNMBzGyNpOuAt4kC4hdEvWaA24BnJF0GjAeWAomgVjcMfzcExkiabmYLgDuBH4FSQD+gN3A/0VvO6oXh5DOBd4HGks4GVpjZREkdf75AZiapB5AI6iNjx34YeCoE3unA5MS+cO1aSaoIDAnD8z/lcCkSf4NhwFtmtk3StUS951OTM0u6hujFMNStWze3y+vcPuujGcu5572ZrN60nes7Hs5NvhiEKySpvKkrDXiV0CuWtA64wswm5lN0Cb/07CDqyS3LJW8PoheQJLQD2ku6HigHlJK00cz6hDYcDZSItaE80IJoIQyAmsBQSV3MLD0MDQ8BLgnBEwAzG0Z4hCsEnqyQvoxfeqLlgG5mti62DzNbKGks0X3vBbEVsbZJepUoqGNm62PHGy7pOUlVgROBLiFIlwYqSHrDzC4yswlA+3D80wmra4W6EstiCvgu/BA7xtrQrs5EowwVJZUIveSf/wZmtipW7EWiLys7MbN+RF8wSEtL8+fP3X5jxYat/O29mXw440eOPKwCr152LC1q+WIQrvCkMmT9CnC9mdU3s/pEgfPVFMp9Q9QTbBBm9vYAhiZnktQUqARMSKSZWS8zqxuOdxvRBKv4LO1f3W82s3VmVjXWxi+BRDCuCHwA3Glmnycdu3r4XYmoV/5S2K4aet0Q9XxfSeRLDEXHguqssH1o+C2i4e8ZYbtmSEsMMxcDVpnZnWZWO7S3BzDGzC5KatdBRD3tF8J2xcQsaeAqYLyZrQ8T7yqGPAcDvwPmhOH8T4iGvSGaHPZevL1BF6KJZc7t98yMgROXcNo/xzN6zgru6NyUd2840YOxK3Sp3CDZYGY/T5wys88k5TtsbWaZkm4kmtVcHHjFzGaGWcPpZpYIzj2BAak+ShX8ATgzxbw3Ao2AeyTdE9JON7MVRMO/R4e0+2MTqToCD0kyoiHrRO/9CODfkrKJAuvDsVnjb0qqRjRUPwW4NqR3B66TlAlsAXqkcK63hyHtYsDzZjYmdvzXJGURfRG4MqQfCvRXNLO9GPC/2L303sAASQ8SDXG/HNJvktSFaMh7NXkM9zu3v1iyZjN/GTKD8fMyOLZ+JR7u1pLDq5Ur7GY5B4Dyiw2S+gJl+GU95AuBNcAgADObVMBtdEVEWlqapaenF3YznNtl2dnG619+zyMfzUFA7zOacdFx9Sjmi0G4vUDSRDNLyy9fKj3kxDO3f0tKP4EoQO80Ecg554qK+Ss20mfQNNK/X8PJTarxj64tqF3JF4NwRU8qs6xP2RsNcc65PWlHVjb9xi/kqVHfcnCp4jxxwdGc37qWLwbhiix/yM45t9+ZsXQddwycxqzl6znrqEO5t8uRVCt/UP4FnStEHpCdc/uNrTuyeGr0t/Qbv5DKZUvxwkVt6NyiZmE3y7mU5BqQJV1gZu9IamBm3+WWzznnioJvFq2m98BpLFy5iT+k1eauM5tzSJmShd0s51KWVw/5TuAdotnUrfdOc5xzbtds3JbJox/N4bUJ31O70sG8ceVxnNS4amE3y7ldlldAXiXpE6CBYssMJphZl4JrlnPO5W/cvAz+Mng6y9Zt4fIT63Pb6U0p6++fdvuovP7LPYuoZ/w60SsYnXOuSFi7eTv3vz+LwZOW0qh6OQZeewJt6lUq7GY595vkGpDDkolfSjohrBdcPkr+eZk/55zbq8yMD2f8yF/fm8HazTu46dRG3HBqIw4q4YtBuH1fKmM7NSSNBCoTvao5A7jUzGYUbNOcc+4XK9Zv5Z73ZjBi5k8cVesQXrviOJofVqGwm+XcHpNKQO4H3GpmnwCEpQL7Eb2pyznnCpSZ8c7EJTz4/iy2ZWZz5xnNuPKkBpQonsraOM7tO1IJyGUTwRjAzMZKKluAbXLOOQAWr97MnYOn89n8lbRtUJmHzz+Khr4YhNtPpRKQF4ZVkl4P2xeRtAavc87tSVnZRv8vFvHYiLkULyYePK8F/9e2ri8G4fZrqYz5XAFUAwaHn6rA5alULqmzpLmS5kvqk8P+vpKmhJ95ktYm7a8gaamkZ8J2+Vj+KZJWSnoy7Ksr6RNJkyVNk3RmSK8S0jcm6onVf2HIO1PSo7H0epJGh31jJdUO6ackHX+rpPPCvlMlTZI0Q1J/SSVCeq9QzzRJX8SWe0wcq3ho8/uxtNzquj127BmSsiRVltQ0qV3rJf05lLkgnF+2pJ1WGwnXbaOk21L5mzpX0L79aQMXvPAF978/i+MbVmbkLSdz0fG+MpM7AJhZgfwQrYG8AGgIlAKmAs3zyP8nojWT42lPAf8FnsmlzETg5PC5H3Bd+NwcWBQ+lwVOIlqf+JlY2SrAD0C1sN0f6BQ+v0M0cQ2i1axez+HYlYnWES5D9MVmMdAk7LsfuDJ8PgGoFD6fAXyVVM+t4RzfD9u51pVU7hxgTC7X/UegXtg+AmgKjAXScsg/KJzvbfn9Tdu0aWPOFZTtmVn2r1HzrPFfhlur+0bYkElLLDs7u7Cb5dxvBqRbCnGzIGdFtAXmm9lCix6hGgCcm0f+nkRrLgMgqQ1QAxiZU2ZJjYHqwKchyYDElMtDgGUAZrbJzD4DtiZV0RCYZ2YZYXsU0C18bg6MDp8/yaXd3YEPzWwzUXDfZmbzwr6PE3WZ2RdmtiakfwnUjp1DbaLnvV+K1ZtrXUl+db1iOgELzOz7cPzZZjY3h3yE3v1CYGZO+53bW6YvWcc5T3/GEx/P4/ctavLxrR047xhfmckdWAoyINci6uklLAlpO5FUD2gAjAnbxYheRnJ7HvX3BN4O3z4A7gUukrQEGE7U487LfKCZpPphSPg8oE7YN5VfgmBXoLykKknle/BLQFwJlIwNCXeP1RV3JfBhbPtJ4A4gO5aWb12SygCdiXq3yeLtylWYmNcbuC+/vM4VlK07snjow9mc++xnrNm8nRcvSePpnsdQtZyvzOQOPAUZkHP6ams5pEEURAaaWVbYvh4YbmaLc8mfKBMPPD2B/5hZbeBM4PUQ2HMUeq3XAW8T9bIXAZlh921AB0mTgQ7A0tg+JB0KHAWMCHVZaE9fSV8DG+L5Q5lTiAJy77B9NrDCzCYmtSvfuoiGqz83s9VJxygFdCEags7PfUBfy+dFL5KukZQuKT0jIyOvrM7tki8XruKMpz7l3+MWcuGxdRh5SwdOa16jsJvlXKHJd5a1pCbA80ANM2shqSXQxcwezKfoEn7ds6tNGEbOQQ/ghth2O6C9pOuBckApSRvNrE9o09FAiaRgdiVRrxEzmyCpNNEEtBW5NdDMhgHDQp3XAFkhfRlwfkgvB3Qzs3Wxon8AhpjZjlhdE4D2oczpQJPEvnDNXgLOMLNVIflEoEuYfFYaqCDpDTO7KK+6Ytcrp17wGcAkM/spt3OOOQ7oHiazVQSyJW01s19NfDOzfkT350lLS8vtC5VzKduwdQcPfziHN7/6gbqVy/Dfq47jhEa+GIRzqfSQXyRa+WkHgJlNIwoI+fkGaCypQei59QB2WqRCUlOgEjAhkWZmvcysrpnVJ+qtvpYIxkFO909/ILp/iqQjiIJcnl06SdXD70pEvfKXwnbVWO/6TuCVpKI7HT9W10FEveAXwnZdotnpF8fuC2Nmd5pZ7XCOPYgmaF2UV10h7RCiXvt7OZxSbveVd2Jm7c2sfjj+k8A/koOxc3vaJ3NWcHrf8bz19Q9cdVIDPvpzew/GzgWpPIdcxsy+TppckTyEuhMzy5R0I9GwbnGiGdQzJd1PNOMsEZx7AgNi94JT8QeiYem4/we8KOkWoqHxyxJ1SlpENOGrVJjIdLqZzQKeij2GdH8sYHYEHpJkwHhivXdJ9Yl6/uOSjn97GIYuBjxvZmNC+l+JJmo9F65hppnt9PhRinVBdE97pJltihcI95VPA/6YlN4VeJro0bUPJE0xs9/nc3zn9qjVm7Zz/7CZvDtlGU1qlOO5XidwTF1fDMK5OOUXByV9CNwIvGNmrSV1J3oM54y90UBXdKSlpVl6enphN8PtQ8yM96ct596hM1m/dQfXd2zEDac0olQJf+2lO3BImphCRyylHvINRPcQm0laSvSWrl6/sX3Ouf3cj+u2cve7Mxg1+yeOrn0Ij3Q/jmY1fTEI53KTSkA2M/tdeEymmJltkNSgoBvmnNs3mRkDvlnMPz6YzY7sbO468wiuOKkBxf1NW87lKZWAPAhonXTPciDQpmCa5JzbV32/ahN9Bk1nwsJVHN+wMg+f35L6VX0tGudSkWtAltQMOBI4RNL5sV0ViGYwO+ccEC0G8ern3/H4yLmULFaMh84/igvT6vj7p53bBXn1kJsCZxM9o3pOLH0DcHVBNso5t++Y++MG7hg0jamL1/K7I6rz4HlHUfMQ/87u3K7KNSCb2XvAe5LahRdVOOfcz7ZnZvPc2Pk8+8l8ypcuyb96HsM5LQ/19087t5tSuYc8WdINRMPXP3/tNbMrCqxVzrkibcritfQeOI25P23g3FaH8bdzjqRy2VKF3Szn9mmpBOTXgTnA74mWAuwFzC7IRjnniqYt27P458dzefmz76hevjQvX5pGpyP8/dPO7QmpBORGZnaBpHPNrL+k/xIWVXDOHTi+WLCSPoOm88PqzfQ6ri69z2hGhdIlC7tZzu03UgnIiQUU1kpqAfwI1C+wFjnnipT1W3fw0PA5vPX1D9SvUoYB1xzP8Q2TVyN1zv1WqQTkfmHxhbuJFocoB9xToK1yzhUJo2b9xF3vTidjwzb+eHJD/vy7JhxcqnhhN8u5/VKeATmseLQ+rB08Hmi4V1rlnCtUqzZu495hsxg2dRnNapbnxUvSaFm7YmE3y7n9Wp4B2cyyw4pN/9tL7XHOFSIzY+jUZdw7dCYbt2Vy62lNuLbD4b4YhHN7QSr/l30s6TZJdSRVTvykUrmkzpLmSpovqU8O+/tKmhJ+5klam7S/gqSlkp6JpZWS1C/knyOpW0i/TFJGrL6rQno9SRND2kxJ18bqulDStJD+aCy9nqTRYd9YSbVj+x6RNCP8XBhLP1XSpJDeX1KJkN5M0gRJ2yTdlsM1KC5psqT3U6jr3NCmKZLSJZ0UK/ORpLXxekK6JP09XK/Zkm4K6R0lrYtdr7/m/dd0+7tla7dwZf90bh4whXpVyvLBTe25qVNjD8bO7SWp3ENOPG98QyzNyGf4WlJx4FmiNXqXAN9IGhrWIY4qMbsllv9PwDFJ1TzAzusO3wWsMLMmYUg9/uXgbTO7MSn/cuAEM9smqRwwQ9JQYBvwGNDGzDJC4OtkZqOBx4HXwqzyU4GHgIslnQW0BloBBwHjwvKUG4H+QCczm6dozedLgZeB1cBNwHm5XKqbiR4jqxCuQ7E86hoNDDUzk9SSaOSiWajnMaAMSeshA5cRrd/cLIx4VI/t+9TMzs6lXe4AkZ1tvPXNDzw0fA5Z2cY9ZzfnshPq+2IQzu1l+X71NbMGOfykci+5LTDfzBaa2XZgAHBuHvl7Am8lNiS1AWoAI5PyXUEUIDGzbDNbmU/7t5vZtrB5EL+cc0NgnpllhO1RQLfwuTlR8AP4JNbu5sA4M8sMi21MBToDVYBtZjYv5Ps4UZeZrTCzb/hltvrPQs/7LOClWHJedW20XxawLkv0xShxnqOJXmua7DrgfjPLTrQnhzzuAPXdyk30fPFL7hoyg6PrHMKIP5/Mlb4yk3OFoiDHomoBi2PbS0LaTiTVAxoAY8J2MeAJ4PakfIlZJQ+EId13JMXfStAtDOkOlFQnVq6OpGmhPY+Y2TJgPtEaz/XDkPB5RD1JiAJtIjh3BcpLqhLSz5BURlJV4JRQZiVQUlJiAerusbry8iRwB5AdS8uzLkldJc0BPuCX0Yu8HA5cGIa4P5TUOLavnaSpIf3IFOpy+4nMrGz+PW4BnZ8cz6zl63m0W0veuPI46lYpU9hNc+6AVZABOaev2JZDGkAPYKCZZYXt64HhZrY4KV8JoDbwuZm1BiYQDS8DDAPqm1lLot5u/58ParY4pDcCLpVUI8wcvw54G/gUWARkhiK3AR0kTQY6AEuBTDMbCQwHviDqzU8I6RbOoa+kr4l6qom6cr440tlEQ+8Tf3WB8qnLzIaYWTOiLxAP5HWM4CBgq5mlAS8Cr4T0SUA9MzsaeBp4N5d2XhOCeXpGRkZOWdw+Zvby9Zz//Bc89OEcTm5SjVG3duAPx9bxd1A7V8gKMiAv4de9xNrAslzy9iA2XA20A26UtIgo4F4i6WFgFbAZGBLyvUN0TxczWxUbmn6RHNZrDj3jmUD7sD3MzI4zs3bAXODbRD4zO9/MjiG6Z42ZrQu//25mrczsNKIvHYkyE8ysvZm1JXpE7Nt8rs+JQJdwjgOAUyW9kWpdZjYeODz01POyhGhNa4iuW8tQfr2ZbQyfhxP1yneqy8z6mVmamaVVq1Ytn0O5omxbZhb/HDmXc57+jKVrtvDs/7Wm38VtqFHBV2ZyrijINyBLGiTprDCMvCu+ARpLaiCpFFHQHZpD/U2BSkS9TQDMrJeZ1TWz+kS91dfMrE/oPQ4DOoasnYBZoZ5DY9V2IbxvW1JtSQeHz5WIAuHcsF09ln494V6upKqx872T0KsMM6KrhM8tiYLbyKS6DgJ6Ay/kdXHM7E4zqx3OsQcwxswuyqsuSY0UujGSWgOliL6k5OVd4NTwuQMwL5SvGaurLdF/C/nV5fZRk35Yw9n/+ox/jZnPOUcfxqhbO3CWr8zkXJGSyizr54HLgX9Jegf4j5nNya+QmWUqeoZ5BFAceMXMZoZZw+lmlgjOPYEBsclK+ekNvC7pSSAjtA3gJkldiIZ3VxPNLgY4AnhCkhH1aB83s+lh31OSjg6f749NpOoIPBTKjOeXGeYlgU/DP2LrgYvMLDGcfHsYhi4GPG9mifvhNYF0olnU2ZL+DDQ3s/V5nGOOdRHd175E0g5gC3Bh4rpJ+pRoxnU5SUuAK81sBPAw8KakW4hmg18V6uoOXCcpM9TVYxf+Bm4fsXl7Jo+PmMerX3zHoRVK8+plx3JKs+r5F3TO7XVK9d9gSYcQBc+7iCZHvQi8YWY7zR52+6e0tDRLT08v7Ga4FH327Ur6DJ7GkjVbuPj4etzRuSnlfTEI5/Y6SRPDPJ48pdJDJgzTXgRcDEwG3gROIno+tuPuN9M5t6et27KDv38wi/+lL6FB1bK8fc3xHOeLQThX5OUbkCUNJhoKfR04x8yWh11vS/LuknNFyIiZP3LPuzNYtWk713Y4nD//rjGlS/piEM7tC1LpIT8Tu4f5K6l0wZ1zBS9jwzbuHTqTD6Yv54hDK/DypcdyVO1DCrtZzrldkEpAPkLSJDNbCz/PSO5pZs8VbNOcc/kxM4ZMXsr9789i87Ysbju9CX/scDgli/v7p53b16QSkK82s2cTG2a2RtLVgAdk5wrR0rVb+Mvg6Yybl0HruhV5tHtLGlUvX9jNcs7tplQCcjFJij1eU5zo+VfnXCHIzjbe+Op7HvlwDgbce05zLm7ni0E4t69LJSCPAP4n6QWiV19eC3xUoK1yzuVoQcZG+gyaxjeL1tC+cVX+0fUo6lT29087tz9IJSD3JlrS7zqiF2uM5NerEznnClhmVjb9Pl3Ik6O+pXSJYjzWvSXd29T2N205tx/JNyCHZfueDz/Oub1s5rJ19B40jRlL19P5yJrcf+6RVPf3Tzu330nlOeTGROsPNwd+/lcgxTWRnXO7aeuOLJ4e8y0vjFtIpTKleL5Xa8446tD8Czrn9kmpDFm/CvwN6Eu0/u/l5Ly0onNuD0lftJo7Bk1jYcYmurWuzT1nH0HFMj6X0rn9WSoB+WAzGx1mWn8P3BsWMvhbAbfNuQPOpm2ZPDZiLv0nLOKwQw6m/xVt6dDEl7107kCQSkDeGpYi/Das3rQU8OVinNvDxs/L4M7B01m2bguXHF+P2zs3o9xBKb1u3jm3H0jldT5/BsoANwFtiBaZuDSVyiV1ljRX0nxJfXLY31fSlPAzT9LapP0VJC2V9EwsbWyoM1Hu57WDJb0djvWVpPohvaSk/pKmS5qt/9/eeYdbVVx9+P1Jr1IlItIUBKIo4VqxolGjUUBRIfYaS9D4fRZIUdQYkahRo/kM9ooFhCBREUUEDRaQ3pQmICiodES4967vj5kDm8MtBwTPgbve59nPnT1lzdpz4Ky9ZubMknolZF0naYqkqTEsYir/QEljYpvXJNXMQNb1Uc4USf0lVY75z0d9p0h6QtJm4XYkHSypQFLXRN7dsf4USeck8n8Xn88k1Uvkd5I0KY7HWElHJsoKEmM1JJH/lKS5ibKDSvs8nR3D8rXrueGViVzwxMdUqrAbr/z2cG7rtL8bY8cpa5hZsRchjvHfSqpTStvZQHPCQSITCXGAi6vfgxAzOZn3APAC4TztVN5IIK+I9lcDj8R0N+ClmP4NId4yhBeLeUBTYH9gSswrD7wNtIj1PgGOielLgDtKkbUXMJcwvQ/wMnBRTJ9CWHMX0B+4Km2MRgCvA11j3qnA8KhTNWIs5VjWLvY3D6iXkFOdTaE02wIzEmWrixnvp1J9Znq1b9/enO3L65MWWfs7hlvzXv+xvm9Ot+/X52dbJcdxtjPAWMvgO7ZED9nMCoD22rYfOx4CzDKzOWa2HngR6FRC/e4EgwWApPZAA8LvnjOhE/B0TA8Ajo96G1BNUnmgCrAeWAm0Bj40s7Vmlg+8B3SJ7fcDRsX0cODMmC5OFgQDWiWWVQUWAZjZ64kP5WOgUULnHsBAYEkirw3wnpnlm9kawovMyVHWeDObl/7gZrY6yodgxDMLcu1kjSUr13Hls+O46vlPaVCzEkN+14EbT2rlkZkcpwyTyZT1eODfks6XdEbqyqDdXsCCxP3CmLcFkpoAzQjeInHN+l7gxmJkPxmnWf+ceFnY2F80sCuAugTjvAZYDMwH7jGz7wje8dGS6kqqSvBk946ypgCnx/RZifwiZZnZl8A9MW8xsMLMNnuRiFPV5xNPOZO0F+EF4JG0Z5sI/EpS1TgtfVyi/2KR1EXSDOA/BK8+ReU4jf2hpM5pze6MU91/l1SptD6cH4+Z8crYBZxw33uMmLmEm50HxUAAABv/SURBVE7ej8HXdODnDT0yk+OUdTJZpKoDfAt0TOQZ8Gop7Yryqovz3LoBA6JHDmH6+XUzW1CEc36umX0pqQbBuzwfeKaE/g4BCoCGQG1gtKS3zWy6pLsJHvBqgiHMj+0uAR6UdAswhOAJU5wsYBnBQ28GLAdekXSemT2X0OWfwCgzGx3v7wduNrOC5DOa2VuSDgb+CywFxiT0KhYzGwQMknQ0cAdwQixqbGaLJDUHRkiabGazgV7AV4TlhH6EE9luT5cr6QrgCoDGjRuXpoZTAgu+W8sfBk1m9OffkNekNnd3bcs+9atnWy3HcXKETE7qungbZS9kc8+uEXEatwi6Adck7g8HjpJ0NWF9tKKk1WbWM3qjmNkqSS8QjOQzif4Wxmnj3YHvCOu+b5rZBmCJpA+APGCOmT0OPA4g6a9RBmY2Azgx5rckrOtSgiwD5prZ0tjmVeAI4Ll4fytQn3AEaYo84MVojOsBp0jKN7PBZnYncGds+wLwefHDvDlmNkrSPpLqmdk3ZpaaOp8jaSRhHXq2mS2OTX6Q9CRwQzHy+hEMNnl5eT4Vvg0UFhrPjJlH32EzEXBHp59z7qFN2M2DQTiOkyCTk7qepAjP1swuKaJ6kk+AFpKaEX4q1Y1g0NLl70fwNsckZJ+bKL+IsImrZzS0tczsmzgF/GvCZiwInuyFUU5XYISZmaT5QEdJzxHWdg8jeKdI2sPMlkhqDJxBeBFI5u8G/IlN08rFyaoCHBanvr8HjidsxkLSZcBJwPEWjiFNPWOzxDM+BQw1s8EK0bRqmdm3ktoSNmmVuI4uaV+CkTVJvyB4vd8qxK5ea2Y/xOnvDkDf2GZPM1scp/w7E6bpne3MrCWruHngZMZ9sYxjWtbnzi7706i2B4NwHGdLMpmyHppIVyasexbn6W7EzPIVfrc8jLCb+AkzmyrpdsKOs9RPcLoTdi5n4n1VAoZFY1yOYIwfjWWPA89KmkXwjLvF/IcJp41NIUxrP2lmk2LZQEl1gQ3ANWa2LKWTpJTH/mpsX6IsSQOATwnTy+OJXiXBmH8BjIne8KtmtsXUcIIKhKlwCBvGzotr4ki6FrgJ+BkwSdLrZnYZYdPZBZI2EF4IzonGuTXwL0mFhP0CfcxsWuzneUn143NMIETxcrYTGwoK6TdqDg+8/TlVK5XjvrMPpEu7vTwYhOM4xaLM7GCiQfAa3zazjqVWdnYp8vLybOzYsdlWI+eZ8uUKbhwwiemLV3LqAXvS+/SfU7+G75lznLKKpHFmlldavW05eaAF4Lt7HCeNdRsKuP/tz3l09BzqVKvII+e15+T9f5ZttRzH2UnIZA15FZuvIX9F2JHrOE7k47nf0XPgJOZ8s4az8xrxx1PasHvVCqU3dBzHiWSyy7rGT6GI4+yMrP4hn7vfmMGzH35Bo9pVeO7SQzmyRb3SGzqO46SRiYfchbBjeUW8rwUca2aDd7RyjpPLvDtzCX98dTKLV67jkg7NuOGkllSt6OdPO46zbWTy7XFrPHQCADNbHn9X6wbZKZMsW7OeO4ZO49XxX7LvHtUZcOURtG9SO9tqOY6zk5OJQS7qeE13A5wyh5nx+uSvuHXIFJav3cC1Hfflmo77Uqm8nz/tOM6PJxPDOlbSfYTf4BohIMK4HaqV4+QYX69cx58HT+GtaV9zwF6788wlh9KmYc1sq+U4zi5EJga5B/Bn4KV4/xbh9CrH2eUxM14eu4C//Gc66/ML6fWrVlx6ZDPKl8skLovjOE7mZLLLeg3Q8yfQxXFyivnfrqXXoEl8MOtbDmlWh7vPbEuzetWyrZbjOLsomeyyHg6cZWbL431twlGXJ+1o5RwnGxQUGk/9dx73DJtJud3EXzrvz28OaezBIBzH2aFkMmVdL2WMAcxsmaQ9dqBOjpM1Pv96FTcNnMT4+cs5br/63NnlABrWqpJttRzHKQNkYpALJTU2s/kAkppQfFxjx9kpWZ9fyCPvzeahEbOoVqkc959zEJ0OaujBIBzH+cnIxCD/EXhf0nvx/mhiwHrH2RWYtHA5Nw2YxIyvVnHagQ259bQ21KvuwSAcx/lpKXWrqJm9CfyCsMv6ZaC9mQ3LRLikkyXNlDRL0hYbwyT9XdKEeH0mKbVO3UTSuJg/VdKViTbnSJoU8/umyTtb0rRY9kJaWU1JX0p6qDRZsf93YtlISY1i/nEJfSdIWiepcywbnchfJGlwzK8taVCU9bGk/dP0KidpvKShibzjJX0aZb0f4x0j6eiYny+pa5qcgkT/QxL5zSR9JOlzSS9JqpjJeJUFvl9fwF2vT6fzwx+wbO16Hr0gj390b+fG2HGc7GBmpV5AbeAQgnd8NHB0Bm3KAbOB5kBFYCLQpoT6PQgxk4n1K8V0dWAe0BCoC8wH6seyp4HjY7oFIQ5x7Xi/R5r8B4AXgIfifUmyXgEujOmOwLNF6FuHEHe5ahFlA4ELYvpvhNPOAFoB76TV/Z+o19BE3mdA65i+GngqppsCbYFngK5pclYXM64vA91i+hHgqkzGq6irffv2tqswZvY3dkzfEdbk5qHWc+BEW/H9+myr5DjOLgow1jKwtaV6yJIuA0YBw4Db4t/epbUjGPBZZjbHzNYDLwKdSqjfHegPYGbrzeyHmF+JTZ58c+AzM1sa798Gzozpy4GHzWxZlLEk8QztgQaE31CTgaw2wDsx/W4xencF3jCztclMSTUIRjx1tOhGWWY2A2gqqUGs2wg4FXgsTbYBqVMndgcWxfbzzGwSUFiEPlugsADaERgQs54GOsd0seO1K7Nq3Qb+OGgy3fp9SKHBC5cdyl1ntKVmZY/M5DhOdsnkdIPrgIOBL8zsOKAdsLTkJgDsBSxI3C+MeVsQN4o1A0Yk8vaWNCnKuNvMFgGzgFaSmkoqTzAue8cmLYGWkj6Q9KGkk6Oc3YB7gRvTui1J1kQ2GecuQA1JddPadyO+QKTRheAFr0zIOiPqcgjQBGgUy+4HbmJLA3sZ8LqkhcD5QJ8i+kmnsqSx8dlTRrcusNzM8uN98jMocrx2ZUbM+JoT/z6K/h/P57IjmzHs90dzxL4emclxnNwgk01d68xsnSQkVTKzGZL2y6BdUdtTi9ud3Q0YYGYFGyuaLQDaSmoIDJY0wMy+lnQVYT27EPgvwdNNPUsL4FiCwRsd12vPA143swXJHbMWfr5VnKwbgIckXUSYHfgSSBk1JO0JHECYLUinO5t7vH2AByRNACYTponzJf0aWGJm4yQdmybjeuAUM/tI0o3AfQQjXRKNzWyRpObACEmTgZVF1Et9BkWOlyV+4haf9QriJr7GjRuXokJu8t2a9dz+2lQGT1hEywbV+ee5R9CusQeDcBwnt8jEIC9UCLk4GBguaRlxCrW0dmzyOCF86RfXrhtwTVEF0chMBY4iGO3XgNdgo7FIGfGFwIdmtgGYK2kmweAcDhwl6WrCenRFSavNrGdxsqI3nvJqqwNnWgw/GTkbGBT72kj0og8heMkp/VcCF8dyAXPj1Q04XdIpQGWgpqTnCMb4QDP7KIp4CXizmHHbbJzi3zmSRhJmMgYCtSSVj15y8jMobrw+SZPbD+gHkJeXt1P93M3MeG3SYnoPmcqqdRu47vgWXHPcvlQs78deOo6Te2Syy7qLmS03s96EM60fZ9M6ZEl8ArSIu3wrEgzQkPRK0duuDYxJ5DWSVCWmawMdgJnxfo9E/tVs8kYHA8fFsnqEKdk5ZnaumTU2s6YEz/cZM+tZkixJ9eJUN0Av4Ik0tTeud6dxFmFz1rrEs9RK7Gy+DBhlZivNrJeZNYp6dSPEnD4PWAbsLqllbPNLYHoRfSXHsLakSoln7wBMi5sJ3iWsdwNcCPy7pPEqqZ+dia9WrOPyZ8Zxbf/x7F27Cq/1OJLrf9nSjbHjODnLVoVRNLP3Sq+1sW6+pN8RpnXLEXZQT5V0O2HHWco4dyccxZn0vloD90oywtT3PWY2OZY9IOnAmL7dzD6L6WHAiZKmETzdG83s21LULE7WscBdsf9RJLx3SU0Jnn9RY9GNLdd7WwPPSCoApgGXlqRQHLfLgYGSCgkG+pLY98HAIMILzGmSbjOzn8c+/hXr7wb0MbNpUeTNwIuS/kKYLn885m/LeOU8ZsaLnyzgr/+ZzobCQv50amsu7tCMcn7speM4OY42t4OOUzx5eXk2duzYbKtRLF98u4aeAyczZs63HN68Ln3OPIAmdT0YhOM42UXSODPLK63eVnnIjpOLFBQaT34wl3vemkmF3XbjrjMOoNvBe/uxl47j7FS4QXZ2amZ+FYJBTFywnBNa78FfOh/Az3avnG21HMdxtho3yM5Oyfr8Qh5+dxb/HDmLGpUr8GD3dpzWdk/3ih3H2Wlxg+zsdExYsJybBkzks69X0/mghtxy2s+pU61i6Q0dx3FyGDfIzk7D9+sLuPetmTzxwVwa1KzMExfl0bFVg2yr5TiOs11wg+zsFPx39jf0HDiZ+d+t5dxDG9PzV62o4edPO46zC+EG2clpVny/gT5vTKf/xwtoWrcqL15xGIc1Tz9W3HEcZ+fHDbKTswyf9jV/GjyZpat+4LdHN+f3J7SkSsVy2VbLcRxnh+AG2ck5vln9A72HTGXopMW0+lkNHr0gj7aNamVbLcdxnB2KG2QnZzAz/j1hEbe9NpXVP+TzP79syZXH7OPnTzuOUyZwg+zkBIuWf8+fBk9hxIwltGtci75ntqVFgxrZVstxHOcnww2yk1UKC40XPp5PnzdmUFBo3PLrNlx4RFMPBuE4TpnDDbKTNeZ+s4abB07i47nf0WHfutzVpS2N61bNtlqO4zhZYYcuzkk6WdJMSbMk9Syi/O+SJsTrM0nLY34TSeNi/lRJVybadJc0WdIkSW/GWL5IOivWLZSUl6hfV9K7klZLeiit/3OinKmS+ibym0h6J5aNlNQoUdY31p8u6UHFsxqjLhNj2SOSysX8OpKGS/o8/q2dpsPBkgokdY33xyXGZIKkdZI6x7LRifxFkgbH/NqSBkV9P5a0f8yvHO9Tet2W6FeS7ozjPl3StVv/CW8b+QWFPPLebE6+fxTTF6+k75ltee7SQ90YO45TtjGzHXIRYiDPBpoDFYGJQJsS6vcgxEwm1q8U09WBeUBDgke/BKgXy/oCvWO6NbAfMBLIS8itBhwJXAk8lMivC8wH6sf7p4HjY/oV4MKY7gg8G9NHAB/EZysHjAGOjWU1418BA4FuCR17xnRP4O60MRoBvA50LWJM6gDfAVWLKBsIXBDTfwNujelWwDsJXarHdAXgI+CweH8x8AywW7zfo7TPtH379vZjmfrlCvv1g6Otyc1D7fKnP7GvVnz/o2U6juPkMsBYy8Bu7kgP+RBglpnNMbP1wItApxLqdwf6A5jZejP7IeZXYpMnr3hVi55pTWBRbDPdzGamCzWzNWb2PrAurag58JmZLY33bwNnxnQb4J2YfjehtwGViS8MBCP3dexnZaxTPpanAk13Ihh74t/OCR16EAzrkiJHBLoCb5jZ2mSmpBqEF4XB6fqa2QygqaQG8d/C6linQrxSel0F3G5mhbFdcTpsF37ID8denv7Q+yxe8T0P/+YX/Ov89jSo6ZGZHMdxYMdOWe8FLEjcL4x5WyCpCdCM4C2m8vaWNCnKuNvMFpnZBoIhmUwwxG2Ax7dRv1lAK0lNJZUnGMq9Y9lENhnnLkANSXXNbAzBQC+O1zAzm57QeRjBuK4CBsTsBma2GCD+3SPW3SvKfqQEHbsRX1LS6ELwglMvAROBM6LcQ4AmQKN4X07ShKjXcDP7KLbZBzhH0lhJb0hqUYIeP4qZX63i1Aff5x8jZnH6QQ0Zfv0xnOqRmRzHcTZjRxrkor5trYg8CIZngJkVbKxotsDM2gL7AhdKaiCpAsEgtyNMYU8Cem2Lcma2LMp6CRhNmBbPj8U3AMdIGg8cA3wJ5EvalzA13ojwctFR0tEJmScBexK8546lqHA/cHPymZNI2hM4ABhWRPHG2YRIH6B2NLw9gPGpZzGzAjM7KOp8SGp9Oeq4zszygEeBJ4rR44potMcuXbq0qCqlUqtqBcrvJp68+GDuO/sgantkJsdxnC3YkbusF7LJ44RgEBYVU7cbcE1RBWa2SNJU4Cjgi5g3G0DSy4R12W3CzF4DXouyrgAKUn2yyeOsDpxpZitinQ9T08CS3gAOA0YlZK6TNIQwVT0c+FrSnma2OBrZ1NRwHvBi9BLrAadIyjez1DT02cCgOCuwEUl1CcsBXRJ9riSsCROn8ufGK/msyyWNBE4GphA+n4GxeBDwZDFj1A/oB5CXl1fcC1WJNKhZmTeuO8o9YsdxnBLYkR7yJ0ALSc0kVSQY3SHplSTtB9QmbJBK5TWSVCWmawMdgJkET7WNpPqx6i+B6WwjklLTx7WBq4HH4n09Samx6cUm73E+wXMuH731Y4DpkqpHY0uc/j4FmBHbDAEujOkLgX8DmFkzM2tqZk0J09tXJ4wxbOkFpzgLGGpmG9fEJdWKYwxwGTDKzFZKqi+pVqxTBTghoddgNnnxxwCflTpgPwI3xo7jOCWzwzxkM8uX9DvClGs5wg7qqZJuJ+w4Sxnn7sCLcSdaitbAvZKMMPV9j5lNBog/3RklaQPBY74o5ncB/gHUB/4jaUKcQkbSPMIGsIrxJ0Qnmtk04AFJB8Y+bzezlFE6Frgr9j+KTd77AIIRm0yYfn/TzF6T1AAYIqkSm3ZOp9aG+wAvS7qUYNDPKm3sJDUlzC68V0RxtygzSWvgGUkFwDTg0pi/J/B0/AnWbsDLZjY0odfzkq4HVhMMueM4jpMltLkddJziycvLs7Fjx2ZbDcdxnJ0KSePifp0S8VP7HcdxHCcHcIPsOI7jODmAG2THcRzHyQHcIDuO4zhODuAG2XEcx3FyAN9l7WSMpKXEw1l2YuoB32RbiRzCx2NzfDw24WOxOT9mPJqYWf3SKrlBdsoUksZm8vODsoKPx+b4eGzCx2Jzforx8Clrx3Ecx8kB3CA7juM4Tg7gBtkpa/TLtgI5ho/H5vh4bMLHYnN2+Hj4GrLjOI7j5ADuITuO4zhODuAG2SkTSNpb0ruSpkuaKum6bOuUbSSVkzRe0tDSa+/axBCmAyTNiP9GDs+2TtlE0vXx/8kUSf0lVc62Tj8lkp6QtETSlEReHUnDJX0e/9be3v26QXbKCvnA/5pZa+Aw4BpJbbKsU7a5jh8RT3wX4wFCONVWwIGU4XGRtBdwLZBnZvsTQsp2y65WPzlPASen5fUE3jGzFsA78X674gbZKROY2WIz+zSmVxG+cPfKrlbZQ1Ij4FTgsWzrkm0k1QSOBh4HMLP1ZrY8u1plnfJAFUnlgarAoizr85NiZqOA79KyOwFPx/TTQOft3a8bZKfMIakp0A74KLuaZJX7gZuAwmwrkgM0B5YCT8Yp/MckVcu2UtnCzL4E7gHmA4uBFWb2Vna1ygkamNliCC/4wB7buwM3yE6ZQlJ1YCDwezNbmW19soGkXwNLzGxctnXJEcoDvwD+z8zaAWvYAdOROwtxbbQT0AxoCFSTdF52tSobuEF2ygySKhCM8fNm9mq29ckiHYDTJc0DXgQ6SnouuypllYXAQjNLzZgMIBjossoJwFwzW2pmG4BXgSOyrFMu8LWkPQHi3yXbuwM3yE6ZQJIIa4TTzey+bOuTTcysl5k1MrOmhM06I8yszHpAZvYVsEDSfjHreGBaFlXKNvOBwyRVjf9vjqcMb3JLMAS4MKYvBP69vTsov70FOk6O0gE4H5gsaULM+4OZvZ5FnZzcoQfwvKSKwBzg4izrkzXM7CNJA4BPCb9OGE8ZO7VLUn/gWKCepIXArUAf4GVJlxJeWs7a7v36SV2O4ziOk318ytpxHMdxcgA3yI7jOI6TA7hBdhzHcZwcwA2y4ziO4+QAbpAdx3EcJwdwg+w4juM4OYAbZMdxsoqkP2xDm7NimMR3d4ROGepQX9JH8fzro7ah/UWSGu4I3ZydEzfIjlPGkFQu2zqksdUGGbgUuNrMjtveymwFxwMzzKydmY3ehvYXEc6KzpgYfcnZRXGD7Di7EJIGSxoXg8tfkchfLel2SR8Bh0s6RdIMSe9LelDS0Fivt6QbEu2mSGoarxkxEtIUSc9LOkHSBzFg+yGxfrUY3P2T6Dl2ivkXSXpV0puxft+Y34cQ5m+CpOeLeJ7ukibHPu+OebcARwKPSPpbWv1jJb0n6WVJn0nqI+lcSR9HOfvEeqclvNu3JTWI+Q9G+Ug6SdIoSVt8T0o6COgLnBJ1ryLpREljJH0q6ZUYyARJt8TxmCKpnwJdgTzC6WCp9vMk1Ytt8iSNTHwm/SS9BTwjqZykv0WZkyT9dmv/nTg5ipn55Zdfu8gF1Il/qwBTgLrx3oCzY7oysABoFu/7A0NjujdwQ0LeFKBpvPKBAwgv8uOAJwARIgMNjvX/CpwX07WAz4BqBG9wDrB77P8LYO9Yb3Uxz9KQcERhfcIxvyOAzrFsJJBXRJtjgeXAnkAl4Evgtlh2HXB/TNdm00mFlwH3xnRVYCpwHDAT2KeEsb4IeCim6wGjgGrx/mbgluRnEtPPAqcV9QzAPKBeTOcBIxOfyTigSry/AvhTTFcCxqY+S7927sunPxxn1+JaSV1iem+gBfAtUECIdAXQCphjZnPjfX/Cl3xpzDWzyQCSpgLvmJlJmkww2AAnEiJJpbzsykDjmH7HzFbE9tOAJoQXg+I4mGCUlsY2zwNHA4NL0fMTi3FrJc0GUrF8JxMMLUAj4KUYtaciMBfAzNZKupxgXK83s9ml9JXiMKAN8EGIx0BFYEwsO07STQRjX4dg8F/LUG6KIWb2fUyfCLSNXjaEl5wWqWdwdl7cIDvOLoKkYwmh8w6PhmUkwSACrDOzglTVEsTks/lSVuVE+odEujBxX8im7xIBZ5rZzDTdDk1rX0Dp3z8l6VkSmej5D+A+MxsSx613os0BhJeYrVnfFTDczLpvlilVBv5J8IQXSOrN5mOaJDn26XXWpPXVw8yGbYV+zk6AryE7zq7D7sCyaIxbEby2opgBNJfUNN6fkyibR4wFLOkXhCD1W8MwoIeimyipXQZtNijEqk7nI+AYSfXiRrTuwHtbqU9x7E6YzoZNIfWQ1AT4X6Ad8Kv4IpEJHwIdJO0b5VSV1JJNhvWbuKbcNdFmFVAjcT8PaB/TZ5bQ1zDgqtSYSWopqVqGejo5jBtkx9l1eBMoL2kScAfBSGxBnPq8GnhT0vvA18CKWDwQqKMQovIqwhrw1nAHUAGYJGlKvC+NfrH+Zpu64rRzL+BdYCLwqZltrxi0vYFXJI0GvoHNYmbfYGaLCDu5H4tebonEafWLgP5x/D8EWpnZcuBRwnT5YOCTRLOnCBvTJkiqAtwGPBB1KqB4HiPEa/40jvG/8NnOXQIPv+g4ZRBJ1c1sdTRCDwOfm9nfs62X45Rl3EN2nLLJ5dELnkqYvv1XlvVxnDKPe8iO4zglIOmPwFlp2a+Y2Z3Z0MfZdXGD7DiO4zg5gE9ZO47jOE4O4AbZcRzHcXIAN8iO4ziOkwO4QXYcx3GcHMANsuM4juPkAP8PyE+POwgd0rUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#随机森林\n",
    "import pandas as pd\n",
    "from numpy import *\n",
    "import operator\n",
    "import numpy as np\n",
    "import sklearn\n",
    "data = pd.read_csv('cleaned.csv',nrows=200000)\n",
    "#print(data)\n",
    "data_list=data.values.tolist()\n",
    "\n",
    "#RandomForestClassifier\n",
    "import math\n",
    "import matplotlib as mpl\n",
    "import warnings\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "x_train=[]\n",
    "y_train_gender=[]\n",
    "y_train_age=[]\n",
    "maxfeature=[]\n",
    "corrate_gender=[]\n",
    "corrate_age=[]\n",
    "for i in range(0,100000):\n",
    "    x=[]\n",
    "    x.append(data_list[i][4])\n",
    "    if data_list[i][5]=='A':\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='B':\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='C':\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "    x.append(data_list[i][6])\n",
    "    x.append(data_list[i][7])\n",
    "    x.append(data_list[i][8])\n",
    "    x.append(data_list[i][9])\n",
    "    x.append(data_list[i][10])\n",
    "    x.append(data_list[i][11])\n",
    "    y_train_gender.append(data_list[i][2])\n",
    "    y_train_age.append(data_list[i][3])\n",
    "    x_train.append(x)\n",
    "for k in range(1,11):\n",
    "    clf2 = RandomForestClassifier(n_estimators=10,max_features=k, max_depth=None,min_samples_split=2, bootstrap=True)\n",
    "    maxfeature.append(k)\n",
    "    scores2 = cross_val_score(clf2, x_train, y_train_gender)\n",
    "    corrate_gender.append(str(scores2.mean()))\n",
    "    \n",
    "    scores2 = cross_val_score(clf2, x_train, y_train_age)\n",
    "    corrate_age.append(str(scores2.mean()))\n",
    "    \n",
    "plt.figure()\n",
    "plt.plot(maxfeature,corrate_gender)\n",
    "plt.xlabel('argument of max_feature')\n",
    "plt.ylabel('accuracy rate of predict gender')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(maxfeature,corrate_age)\n",
    "plt.xlabel('argument of max_feature')\n",
    "plt.ylabel('accuracy rate of predict age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "性别样本总数： 100000 错误样本数 : 24472\n",
      "预测正确率：0.755280\n",
      "年龄样本总数： 100000 错误样本数 : 62151\n",
      "预测正确率：0.378490\n"
     ]
    }
   ],
   "source": [
    "#朴素贝叶斯\n",
    "import pandas as pd\n",
    "from numpy import *\n",
    "import operator\n",
    "import numpy as np\n",
    "import sklearn\n",
    "data = pd.read_csv('cleaned.csv',nrows=200000)\n",
    "#print(data)\n",
    "data_list=data.values.tolist()\n",
    "x_train = []\n",
    "y_train_gender = []\n",
    "y_train_age = []\n",
    "for i in range(0,100000):\n",
    "    x=[]\n",
    "    x.append(data_list[i][4])\n",
    "    if data_list[i][5]=='A':\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='B':\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='C':\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "    x.append(data_list[i][6])\n",
    "    x.append(data_list[i][7])\n",
    "    x.append(data_list[i][8])\n",
    "    x.append(data_list[i][9])\n",
    "    x.append(data_list[i][10])\n",
    "    x.append(data_list[i][11])\n",
    "    y_train_gender.append(data_list[i][2])\n",
    "    y_train_age.append(data_list[i][3])\n",
    "    x_train.append(x)\n",
    "#预测性别\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "clf = GaussianNB()\n",
    "clf = clf.fit(x_train, y_train_gender)\n",
    "y_pred=clf.predict(x_train)\n",
    "print(\"性别样本总数： %d 错误样本数 : %d\" % (len(x_train),(y_train_gender != y_pred).sum()))\n",
    "k=(len(x_train)-(y_train_gender != y_pred).sum())\n",
    "print(\"预测正确率：%f\" % (k/len(x_train)))\n",
    "\n",
    "#预测年龄\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "clf = GaussianNB()\n",
    "clf = clf.fit(x_train, y_train_age)\n",
    "y_pred=clf.predict(x_train)\n",
    "print(\"年龄样本总数： %d 错误样本数 : %d\" % (len(x_train),(y_train_age != y_pred).sum()))\n",
    "k=(len(x_train)-(y_train_age != y_pred).sum())\n",
    "print(\"预测正确率：%f\" % (k/len(x_train)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测性别正确率：0.720000\n",
      "预测年龄正确率：0.422000\n"
     ]
    }
   ],
   "source": [
    "#决策树\n",
    "import pandas as pd\n",
    "from numpy import *\n",
    "import operator\n",
    "import numpy as np\n",
    "import sklearn\n",
    "data = pd.read_csv('cleaned.csv',nrows=200000)\n",
    "#print(data)\n",
    "data_list=data.values.tolist()\n",
    "x_train = []\n",
    "y_train_gender = []\n",
    "y_train_age = []\n",
    "for i in range(0,100000):\n",
    "    x=[]\n",
    "    x.append(data_list[i][4])\n",
    "    if data_list[i][5]=='A':\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='B':\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='C':\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "    x.append(data_list[i][6])\n",
    "    x.append(data_list[i][7])\n",
    "    x.append(data_list[i][8])\n",
    "    x.append(data_list[i][9])\n",
    "    x.append(data_list[i][10])\n",
    "    x.append(data_list[i][11])\n",
    "    y_train_gender.append(data_list[i][2])\n",
    "    y_train_age.append(data_list[i][3])\n",
    "    x_train.append(x)\n",
    "pre=[]\n",
    "for i in range(100000,101000):\n",
    "    x=[]\n",
    "    x.append(data_list[i][4])\n",
    "    if data_list[i][5]=='A':\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='B':\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='C':\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "    x.append(data_list[i][6])\n",
    "    x.append(data_list[i][7])\n",
    "    x.append(data_list[i][8])\n",
    "    x.append(data_list[i][9])\n",
    "    x.append(data_list[i][10])\n",
    "    x.append(data_list[i][11])\n",
    "    pre.append(x)\n",
    "from sklearn import tree\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "#预测性别\n",
    "clf = clf.fit(x_train, y_train_gender)\n",
    "h=clf.predict(pre)\n",
    "sumdata=1000\n",
    "correct=0\n",
    "j=100000\n",
    "for i in h:\n",
    "    if i==data_list[j][2]:\n",
    "        correct=correct+1\n",
    "    j=j+1\n",
    "print(\"预测性别正确率：%f\" %(correct/sumdata))\n",
    "#预测年龄\n",
    "clf = clf.fit(x_train, y_train_age)\n",
    "h=clf.predict(pre)\n",
    "correct=0\n",
    "j=100000\n",
    "for i in h:\n",
    "    if i==data_list[j][3]:\n",
    "        correct=correct+1\n",
    "    j=j+1\n",
    "print(\"预测年龄正确率：%f\" %(correct/sumdata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'accuracy rate of predict age')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KNN\n",
    "import pandas as pd\n",
    "from numpy import *\n",
    "import operator\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn import datasets\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "data = pd.read_csv('cleaned.csv',nrows=200000)\n",
    "#print(data)\n",
    "data_list=data.values.tolist()\n",
    "x_train = []\n",
    "y_train_gender = []\n",
    "y_train_age=[]\n",
    "for i in range(0,100000):\n",
    "    x=[]\n",
    "    x.append(data_list[i][4])\n",
    "    if data_list[i][5]=='A':\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='B':\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='C':\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "    x.append(data_list[i][6])\n",
    "    x.append(data_list[i][7])\n",
    "    x.append(data_list[i][8])\n",
    "    x.append(data_list[i][9])\n",
    "    x.append(data_list[i][10])\n",
    "    x.append(data_list[i][11])\n",
    "    y_train_gender.append(data_list[i][2])\n",
    "    y_train_age.append(data_list[i][3])\n",
    "    x_train.append(x)\n",
    "pre=[]\n",
    "for i in range(100000,101000):\n",
    "    x=[]\n",
    "    x.append(data_list[i][4])\n",
    "    if data_list[i][5]=='A':\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='B':\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "        x.append(0)\n",
    "    if data_list[i][5]=='C':\n",
    "        x.append(0)\n",
    "        x.append(0)\n",
    "        x.append(1)\n",
    "    x.append(data_list[i][6])\n",
    "    x.append(data_list[i][7])\n",
    "    x.append(data_list[i][8])\n",
    "    x.append(data_list[i][9])\n",
    "    x.append(data_list[i][10])\n",
    "    x.append(data_list[i][11])\n",
    "    pre.append(x)\n",
    "resu_gender=[]\n",
    "resu_age=[]\n",
    "knn_argu=[]\n",
    "for knnargu in range(1,50):\n",
    "    knn_argu.append(knnargu)\n",
    "    knn=KNeighborsClassifier(n_neighbors=knnargu)\n",
    "    #预测性别\n",
    "    knn.fit(x_train,y_train_gender)\n",
    "    resu=[]\n",
    "    resu=knn.predict(pre)\n",
    "    #print(resu)\n",
    "    k=1000\n",
    "    h=0\n",
    "    j=0\n",
    "    for i in range(100000,101000):       \n",
    "        if data_list[i][2]==resu[j]:\n",
    "            h=h+1\n",
    "        j=j+1\n",
    "    resu_gender.append(h/k)\n",
    "        #print(\"预测性别准确率为：%f\"% (h/k))\n",
    "        #预测年龄\n",
    "    knn.fit(x_train,y_train_age)\n",
    "    resu=knn.predict(pre)\n",
    "    h=0\n",
    "    j=0\n",
    "    for i in range(100000,101000):\n",
    "        if data_list[i][3]==resu[j]:\n",
    "            h=h+1\n",
    "        j=j+1\n",
    "    resu_age.append(h/k)\n",
    "plt.figure()\n",
    "plt.plot(knn_argu,resu_gender)\n",
    "plt.xlabel('argument of k')\n",
    "plt.ylabel('accuracy rate of predict gender')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(knn_argu,resu_age)\n",
    "plt.xlabel('argument of k')\n",
    "plt.ylabel('accuracy rate of predict age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier交叉验证准确率为:0.9098972922502334\n"
     ]
    }
   ],
   "source": [
    "#RandomForestClassifier\n",
    "import math\n",
    "import matplotlib as mpl\n",
    "import warnings\n",
    "import numpy as np\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "n_features=2  #每个样本有几个属性或特征\n",
    "x,y = make_blobs(n_samples=300, n_features=n_features, centers=6)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, train_size=0.7)\n",
    "clf2 = RandomForestClassifier(n_estimators=10,max_features=math.sqrt(n_features), max_depth=None,min_samples_split=2, bootstrap=True)\n",
    "scores2 = cross_val_score(clf2, x_train, y_train)\n",
    "print('RandomForestClassifier交叉验证准确率为:'+str(scores2.mean()))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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